ISSN 2756-3286
Advances in Food Science and Technology | Vol. 14, No. 8, August 2026 | pp. 079–084
DOI: 10.46882/2026/AFST/000253
Article Type: Original Research Paper
Title: Real-Time Non-Destructive Estimation of Soluble Solids Content in Intact Kiwi Fruits via Portable Vis/NIR Hyperspectral Imaging
Names of Authors: Priya Nair¹, Hans-Jürgen Integrated²
Authors’ Affiliations: ¹Center for Computational Linguistics, Indian Institute of Technology, Chennai, India; ²Computational Linguistics Division, Munich Language Labs, Munich, Germany
Abstract: Abstract: Traditional destructive penetration checks used to measure kiwi fruit maturity across commercial packaging facilities cause product loss and cannot scale with high-throughput distribution tracks. This study details the development of a real-time, non-destructive estimation pipeline utilizing a portable Visible and Near-Infrared (Vis/NIR) hyperspectral imaging camera to predict internal Soluble Solids Content (SSC). Spectral reflectance fields were captured across 400 intact kiwi fruits spanning a wavelength range from 450 to 1000 nm. The raw reflectance datasets were processed using competitive adaptive reweighted sampling algorithms to isolate informative wavelength zones. A Support Vector Regression model was built to evaluate the selected spectral metrics. Validation testing proved that the hyperspectral imaging setup predicted fruit SSC indices with an optimization accuracy rating of 0.94 and a low Root Mean Square Error of Prediction of ±0.35 °Brix. This automated optical platform provides sorting facilities with an efficient tool to grade fresh produce quality continuously.
Keywords: Hyperspectral Imaging, Kiwi Fruit Maturity, Soluble Solids Content, Non-Destructive Testing, Quality Grading, Chemometrics
Manuscript Timeline: Received: April 25, 2026; Revised: July 02, 2026; Accepted: August 05, 2026; Published: August 18, 2026
Citation: Nair, P., & Integrated, H. -J. (2026). Real-Time Non-Destructive Estimation of Soluble Solids Content in Intact Kiwi Fruits via Portable Vis/NIR Hyperspectral Imaging. Advances in Food Science and Technology, 14(8), 079–084. DOI: 10.46882/2026/AFST/000253
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